#!/usr/bin/python3
# -*- coding:utf-8 -*-
# Copyright (c) Huawei Technologies Co., Ltd. 2025. All rights reserved.
# This file is a part of the CANN Open Software.
# Licensed under CANN Open Software License Agreement Version 1.0 (the "License").
# Please refer to the License for details. You may not use this file except in compliance with the License.
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
# INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
# See LICENSE in the root of the software repository for the full text of the License.
# ======================================================================================================================

import os
import torch
import numpy as np

def gen_golden_data_simple():
    dtype = torch.float
    gradoutputshape = (1, 4)
    selfshape = (1, 2)
    gradintputshape = (1, 2)
    gradoutputvalues = [1, 1, 1, 1]
    selfvalue = [1, 2]
    gradintputvalues = [0, 0]

    padding = (1, 1)

    gradoutput = torch.tensor(gradoutputvalues, dtype=torch.float).reshape(gradoutputshape)
    gradinput = torch.tensor(gradintputvalues, dtype=torch.float).reshape(gradintputshape)
    grad_input = torch.ops.aten.reflection_pad1d_backward(gradoutput, gradinput, padding)
    golden = grad_input.numpy()

    os.system("mkdir -p input")
    os.system("mkdir -p output")
    golden.tofile("./output/golden.bin")
    print(golden)

if __name__ == "__main__":
    gen_golden_data_simple()

